protected internal void accumulate(int num, TrainerScore[] array, TrainerScore[] array2)
{
TrainerScore trainerScore = array[num];
this.currentLogLikelihood = 0f;
this.logLikelihood -= array[0].getScalingFactor();
SenoneHMMState senoneHMMState = (SenoneHMMState)trainerScore.getState();
if (senoneHMMState == null)
{
int num2 = trainerScore.getSenoneID();
if (num2 == -1)
{
this.accumulateMean(num2, array[num]);
this.accumulateVariance(num2, array[num]);
this.accumulateMixture(num2, array[num]);
this.accumulateTransition(num2, num, array, array2);
}
}
else if (senoneHMMState.isEmitting())
{
int num2 = this.senonePool.indexOf(senoneHMMState.getSenone());
this.accumulateMixture(num2, array[num]);
this.accumulateTransition(num2, num, array, array2);
}
}
private void accumulateMixture(int num, TrainerScore trainerScore)
{
if (num == -1)
{
for (int i = 0; i < this.senonePool.size(); i++)
{
this.accumulateMixture(i, trainerScore);
}
}
else
{
Buffer buffer = (Buffer)this.mixtureWeightsBufferPool.get(num);
for (int j = 0; j < this.mixtureWeights.getGauPerState(); j++)
{
float num2 = trainerScore.getComponentGamma()[j];
num2 -= this.currentLogLikelihood;
buffer.logAccumulate(num2, j, this.logMath);
}
}
}
private void accumulateMean(int num, TrainerScore trainerScore)
{
if (num == -1)
{
for (int i = 0; i < this.senonePool.size(); i++)
{
this.accumulateMean(i, trainerScore);
}
}
else
{
GaussianMixture gaussianMixture = (GaussianMixture)this.senonePool.get(num);
MixtureComponent[] mixtureComponents = gaussianMixture.getMixtureComponents();
for (int j = 0; j < mixtureComponents.Length; j++)
{
float[] mean = mixtureComponents[j].getMean();
int num2 = ((Integer)this.indexMap.get(mean)).intValue();
if (!HMMPoolManager.assertionsDisabled && num2 < 0)
{
throw new AssertionError();
}
if (!HMMPoolManager.assertionsDisabled && num2 != num)
{
throw new AssertionError();
}
Buffer buffer = (Buffer)this.meansBufferPool.get(num2);
float[] values = ((FloatData)trainerScore.getData()).getValues();
double[] array = new double[values.Length];
float num3 = trainerScore.getComponentGamma()[j];
num3 -= this.currentLogLikelihood;
double num4 = this.logMath.logToLinear(num3);
for (int k = 0; k < array.Length; k++)
{
array[k] = (double)values[k] * num4;
}
buffer.accumulate(array, num4);
}
}
}
private void accumulateVariance(int num, TrainerScore trainerScore)
{
if (num == -1)
{
for (int i = 0; i < this.senonePool.size(); i++)
{
this.accumulateVariance(i, trainerScore);
}
}
else
{
GaussianMixture gaussianMixture = (GaussianMixture)this.senonePool.get(num);
MixtureComponent[] mixtureComponents = gaussianMixture.getMixtureComponents();
for (int j = 0; j < mixtureComponents.Length; j++)
{
float[] mean = mixtureComponents[j].getMean();
float[] variance = mixtureComponents[j].getVariance();
int id = ((Integer)this.indexMap.get(variance)).intValue();
Buffer buffer = (Buffer)this.varianceBufferPool.get(id);
float[] values = ((FloatData)trainerScore.getData()).getValues();
double[] array = new double[values.Length];
float num2 = trainerScore.getComponentGamma()[j];
num2 -= this.currentLogLikelihood;
double num3 = this.logMath.logToLinear(num2);
for (int k = 0; k < array.Length; k++)
{
array[k] = (double)(values[k] - mean[k]);
double[] array2 = array;
int num4 = k;
double[] array3 = array2;
array3[num4] *= array[k] * num3;
}
buffer.accumulate(array, num3);
}
}
}
